Dan Steinberg

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.

A discrete choice contingent valuation estimate of the value of Kenai King salmon

Abstract A new method for estimating the demand curve for publicly supplied goods when quantities are restricted to a few discrete levels is introduced. The method involves fitting a conditional logit model to choices from a set of survey options in which price and quantity are both varied and consumer attitudes are explicitly controlled. The estimated parameters of the valuation function serve to trace the marginal value of the good at each level of hypothetical consumption in survey data. We apply the method to the valuation of salmon on Alaskas Kenai River. We find that there is a distinct kink in the marginal valuation function and that sport fishermen may place a negative marginal value on fish permits exceeding their desired catch levels.

Estimating logistic regression models when the dependent variable has no variance

We show that the binary logistic regression model can often be estimated even when the study sample is confined to observations on only one of the possible outcomes of the dependent variable. Provided that an appropriate supplementary sample can be found, the two samples may be pooled, and a simple method employed to estimate the model with a conventional statistics package. The supplementary sample must contain information on the regressors of the model, but need not contain any information on the dependent variable. Hence supplementary samples can often be found in general purpose public use surveys such as the U.S. Census.

KDD-Cup 2000: question 2 winner's report Salfor sytems

Responding to the challenge required an extended process of data preparation and exploratory data analysis, combining our data with other information from the US Census, deciding on the specific contrasts to study (high spender vs. low spender, high spender vs. non-spender), and running a series of CART models to separate the high spenders from other groups. This section briefly describes the steps we took to arrive at our final analysis.